 kappa_list = [500];
 num_train_list = [8];
 noise_level = [0];
 beta = 1;
 %kappa_list = [200];
 %num_train_list = [512];
file_ID = fopen('result/out_low_noise.txt', 'w');
for ii=1:length(kappa_list)
    kappa = kappa_list(ii);
    for jj=1:8
        %num_train_real = num_train_list(jj);
        num_train_real = ceil(num_train_list(1)*kappa^beta)+1;
        noise = noise_level(jj);
        %% generate data  and 1st
        is_saved = jj;
        num_train = num_train_real;
        generate_simple_data

        % l1st_app
        % l2st_app
        % name = sprintf('simple_example_kappa_%d_N_%d.mat\n', kappa, num_train);
        % fprintf(file_ID, name);
        % t1 = cputime;
        % l1st_app
        % time1 = cputime-t1;
        % res1 = sprintf('1 %e %6.2f\n', err1, time1);
        % fprintf(file_ID, res1);
        % 
        % 
        % %2st
        % is_saved = 0;
        % num_train = 2*ceil(num_train_real/2)+1;
        % generate_simple_data
        % t2 = cputime;
        % l2st_app
        % time2 = cputime-t2;
        % res2 = sprintf('2 %e %6.2f\n', err2, time2);
        % fprintf(file_ID, res2);
        % 
        % 
        % %3st
        % is_saved = 0;
        % num_train = 3*ceil(num_train_real/3)+1;
        % generate_simple_data
        % t3 = cputime;
        % l3st_app
        % time3 = cputime-t3;
        % res3 = sprintf('3 %e %6.2f\n', err3, time3);
        % fprintf(file_ID, res3);
        % 
        % 
        % fprintf('\n')
        

    end
end


fclose(file_ID);